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Iterative Min Cut Clustering Based on Graph Cuts

Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for...

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Detalles Bibliográficos
Autores principales: Liu, Bowen, Liu, Zhaoying, Li, Yujian, Zhang, Ting, Zhang, Zhilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827042/
https://www.ncbi.nlm.nih.gov/pubmed/33440849
http://dx.doi.org/10.3390/s21020474
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author Liu, Bowen
Liu, Zhaoying
Li, Yujian
Zhang, Ting
Zhang, Zhilin
author_facet Liu, Bowen
Liu, Zhaoying
Li, Yujian
Zhang, Ting
Zhang, Zhilin
author_sort Liu, Bowen
collection PubMed
description Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.
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spelling pubmed-78270422021-01-25 Iterative Min Cut Clustering Based on Graph Cuts Liu, Bowen Liu, Zhaoying Li, Yujian Zhang, Ting Zhang, Zhilin Sensors (Basel) Communication Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time. MDPI 2021-01-11 /pmc/articles/PMC7827042/ /pubmed/33440849 http://dx.doi.org/10.3390/s21020474 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Liu, Bowen
Liu, Zhaoying
Li, Yujian
Zhang, Ting
Zhang, Zhilin
Iterative Min Cut Clustering Based on Graph Cuts
title Iterative Min Cut Clustering Based on Graph Cuts
title_full Iterative Min Cut Clustering Based on Graph Cuts
title_fullStr Iterative Min Cut Clustering Based on Graph Cuts
title_full_unstemmed Iterative Min Cut Clustering Based on Graph Cuts
title_short Iterative Min Cut Clustering Based on Graph Cuts
title_sort iterative min cut clustering based on graph cuts
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827042/
https://www.ncbi.nlm.nih.gov/pubmed/33440849
http://dx.doi.org/10.3390/s21020474
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AT liyujian iterativemincutclusteringbasedongraphcuts
AT zhangting iterativemincutclusteringbasedongraphcuts
AT zhangzhilin iterativemincutclusteringbasedongraphcuts